The sense of agency (SoA) is part of psychophysiological modules related to the self. Disturbed SoA is found in several clinical conditions, hence understanding the neural correlates of the SoA is useful for the diagnosis and determining the proper treatment strategies. Although there are several neuroimaging studies on SoA, it is desirable to translate the knowledge to more accessible and inexpensive EEG-based biomarkers for the sake of applicability. However, SoA has not been widely investigated using EEG. To address this issue, we designed an EEG experiment on healthy adults (n = 15) to determine the sensitivity of EEG on the SoA paradigm using hand movement with parametrically delayed visual feedback. We calculated the power spectral density over the traditional EEG frequency bands for ten delay conditions relative to no delay condition. Independent component analysis and equivalent current dipole modeling were applied to address artifact rejection, volume conduction, and source localization to determine the effect of interest. The results revealed that the alpha and low-beta EEG power increased in the parieto-occipital regions in proportion to the reduced SoA reported by the subjects. We conclude that the parieto-occipital alpha and low-beta EEG power reflect the sense of agency.

The Anti-Nuclear Antibodies (ANAs) testing is the primary serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted mainly by the Indirect Immunofluorescence (IIF) on Human Epithelial cell-substrate (HEp-2) protocol. However, due to its high variability, human-subjectivity, and low throughput, there is an insistent need to develop an efficient Computer-Aided Diagnosis system (CADs) to automate this protocol. Many recently proposed Convolutional Neural Networks (CNNs) demonstrated promising results in HEp-2 cell image classification, which is the main task of the HE-p2 IIF protocol. However, the lack of large labeled datasets is still the main challenge in this field. This work provides a detailed study of the applicability of using generative adversarial networks (GANs) algorithms as an augmentation method. Different types of GANs were employed to synthesize HEp-2 cell images to address the data scarcity problem. For systematic comparison, empirical quantitative metrics were implemented to evaluate different GAN models’ performance of learning the real data representations. The results of this work showed that though the high visual similarity with the real images, GANs’ capacity to generate diverse data is still limited. This deficiency in the generated data diversity is found to be of a crucial impact when used as a standalone method for augmentation. However, combining limited-size GANs-generated data with classic augmentation improves the classification accuracy across different variants of CNNs. Our results demonstrated a competitive performance for the overall classification accuracy and the mean class accuracy of the HEp-2 cell image classification task.

Pain management is a critical challenge in healthcare, often exacerbated by loneliness and emotional distress. This study investigated the potential of a communication robot, Moffuly, to reduce pain perception and influence hormonal responses in a controlled experimental setting. Nineteen healthy participants underwent heat pain stimulation under two conditions: with and without robotic interaction. Pain levels were assessed using the Short-form McGill Pain Questionnaire and the Visual Analogue Scale, while mood and mental states were evaluated through established questionnaires including the Profile of Mood States, Hospital Anxiety and Depression Scale, and Self-Rating Depression Scale. Hormonal changes, including cortisol, growth hormone, oxytocin, estradiol, and dehydroepiandrosterone-sulfate, were measured from blood samples collected at key time points. The results demonstrated significant reductions in subjective pain and improvements in mood following robotic interaction. These effects were accompanied by favorable hormonal changes, including increased oxytocin and decreased cortisol and growth hormone levels. The findings suggest that robotic interaction may serve as an innovative approach to pain management by addressing both physiological and psychological factors. This study highlights the potential of robotics to complement traditional therapies in alleviating pain and enhancing emotional well-being. By mitigating emotional distress and loneliness, robotic interventions may enhance existing pain therapies and offer innovative solutions for resource-limited healthcare systems.

Introduction: The new ICD-11 code for chronic pain indicates a direction to divide chronic pain into two categories: chronic secondary pain, which has a clear underlying disease, and chronic primary pain, which is associated with significant emotional distress or functional disability and cannot be explained by another chronic condition. Until now, epidemiological studies have been hampered by the lack of a clear classification, but we believe that this new code system will provide a new perspective on the diagnosis and treatment of chronic pain, and we have begun work on this code system.Methods: We studied 2,360 patients at Aichi Medical University, the largest pain center in Japan, and asked them to answer questionnaires on pain severity (NRS), pain-related functional impairment (PDAS, Locomo25), quality of life (EQ-5D), and psychological state and pain cognition (HADS, PCS, PSEQ, AIS) while their attending physicians were giving diagnoses according to ICD-11 and the results of the study were used to determine the coding of pain severity.Results and discussion: The ratio of primary to chronic secondary pain was almost 50%, and the group of patients with MG30.01 classification, which included fibromyalgia, had the highest severity among chronic primary pain. The MG30.01 classification of patients was also found to experience more severe pain compared to other classifications of chronic primary pain patients. The classification of patients with a major psychiatric component was not always clear, and some patients in the secondary category also had a clear psychiatric component, suggesting the need to develop complementary tools to support pain diagnosis.

Spectral subtraction denoising as a new preprocessing block for Slow Cortical Potential(SCP)-based brain computer interface is proposed. This method adaptively reduces the noise attached to the signal using spectral subtraction denoising. We show that this technique provides better performance when low number of electrodes is used. This suggests its potential application in portable BCI real time applications where low power and minimum weight are required for practical use. Here, the classification accuracy was used as performance measure. The developed method provides relatively better performance compared to the more commonly used wavelet shrinkage signal denoising.